| 2026 | ISIT | Machine Unlearning for Gibbs Supervised Learning Algorithms. | Yaiza Bermudez, Samir M. Perlaza, Iaki Esnaola |
| 2026 | ISIT | Decentralized Machine Learning with Centralized Performance Guarantees via Gibbs Algorithms. | Yaiza Bermudez, Samir M. Perlaza, Iaki Esnaola |
| 2025 | ITW | A Dual Optimization View to Empirical Risk Minimization with f-Divergence Regularization. | Francisco Daunas, Iaki Esnaola, Samir M. Perlaza |
| 2024 | AAAI | Generalization Analysis of Machine Learning Algorithms via the Worst-Case Data-Generating Probability Measure. | Xinying Zou, Samir M. Perlaza, Iaki Esnaola, Eitan Altman |
| 2024 | ISIT | Equivalence of Empirical Risk Minimization to Regularization on the Family of $f- \text{Divergences}$. | Francisco Daunas, Iaki Esnaola, Samir M. Perlaza, H. Vincent Poor |
| 2023 | ISIT | Analysis of the Relative Entropy Asymmetry in the Regularization of Empirical Risk Minimization. | Francisco Daunas, Iaki Esnaola, Samir M. Perlaza, H. Vincent Poor |
| 2023 | ISIT | On the Validation of Gibbs Algorithms: Training Datasets, Test Datasets and their Aggregation. | Samir M. Perlaza, Iaki Esnaola, Gaetan Bisson, H. Vincent Poor |
| 2022 | ISIT | Empirical Risk Minimization with Relative Entropy Regularization: Optimality and Sensitivity Analysis. | Samir M. Perlaza, Gaetan Bisson, Iaki Esnaola, Alain Jean-Marie, Stefano Rini |
| 2021 | ICIP | Compressive Covariance Matrix Estimation from a Dual-Dispersive Coded Aperture Spectral Imager. | Jonathan Monsalve, Miguel Marquez, Iaki Esnaola, Henry Arguello |
| 2019 | ISIT | Universal Privacy Guarantees for Smart Meters. | Miguel Arrieta, Iaki Esnaola, Michelle Effros |